A biological nervous system comprises a complex network of neurons that receive and process input signals received from external stimuli to process and store information. A biological nervous system can be described as a large hierarchical array forming a probable content addressable associative memory. A neuron is a specialized cell capable of communicating with other cells. A neuron can be described as a cell body called soma, having one or more dendrites as terminals for input signals and an axon as an output terminal. One dendrite of a neuron and one axon of another neuron are connected by a biological structure called a synapse. The soma of a neuron produces a variable set of pulses of a particular frequency and interval known as action potentials when triggered by a signal or the sum of potentials received from a plurality of synapses, connected to dendrites, thereby allowing one neuron to communicate with a plurality of other neurons. Synapses can be excitatory or inhibiting. In this manner a neural network comprises a plurality of neurons that are interconnected by synapses. A plurality of networked neurons is triggered in an indicative spatial and temporal activation pattern as a result of a specific input signal pattern. Each input pulse relates to an event. An event can be described as the occurrence of a specific frequency in an audio stream, the occurrence of a dark to light transition in visual information, and a plethora of other phenomena. Feedback of output pulses to synaptic inputs drives a process known as Synaptic Time Dependent Plasticity, commonly abbreviated as STDP, whereby the strength of a synapse is modified depending on the temporal different of input to output pulses. This process is thought to be responsible for learning and memory functions in the brain. Massive feedback connections attach neurons at lower layers to events at higher regions. Event phenomena at higher levels in the hierarchy are more complex. Instead of triggering on the occurrence of a specific frequency, the inputs to a higher-level neuron represent the combined output of neurons at lower levels and it triggers on a phoneme. A brain can be modeled as a neural network with massive feed-forward and feedback connections, which processes information by the spatial and temporal activation pattern of neurons in the network. The human brain contains an estimated 1011 neurons interconnected through an estimated 1014 synaptic connections.
One description of the operation of a general neural network is; a context addressable associative memory system wherein the content is dynamically derived from the probability of input patterns to stored synaptic strengths. An action potential is generated in the post-synaptic neuron when an input pulse causes sufficient positively charged neurotransmitters to be released into the synaptic deft. The synaptic cleft is the space between the synapse and the dendrite of a neuron cell. The synaptic potentials of all synapses are integrated to produce a summed membrane potential. The membrane potential is slowly discharging towards the rest state, and temporally recharged by subsequent pulses. Inhibiting synapses have the opposite effect, causing the membrane potential to be lowered toward, or below the rest potential and making it less likely that the soma will produce an action potential. The neuron soma produces an action potential when the rate of discharge and subsequent recharging results in a membrane potential that matches or exceeds a predefined but variable threshold. The neuron generates a pulse train that has a typical duration and interval period. This pulse train then propagates through one or more axons to the synapses of other neurons. Each neuron secretes only one particular neurotransmitter, which is either excitatory or inhibiting. Feedback channels modify the properties of the neuron to strengthen or weaken the interaction between neurons and cause a variation in the membrane threshold value. Action potentials form precise temporal patterns or sequences as spike trains. The temporal properties of spikes are indicative of the selection of specific neurons within the hierarchy in a process referred to as ‘Neuro-percolation’. The coordinated activity of a large section of the population of neurons is required to express information in a biological neural network. The above process forms the basis for information processing, storage, recall and exchange in biological neural networks.